Data to 7 Oct 2026

Hierarchical Generation of Molecular Graphs using Structural Motifs

arXiv 2002.03230 ↗First posted 8 Feb 2020cs.LG stat.MLcited by 204

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Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Graph generation techniques are increasingly being adopted for drug discovery. Previous graph generation approaches have utilized relatively small molecular building blocks such as atoms or simple cycles, limiting their effectiveness to smaller molecules. Indeed, as we demonstrate, their performance degrades significantly for larger molecules. In this paper, we propose a new hierarchical graph encoder-decoder that employs significantly larger and more flexible graph motifs as basic building blocks. Our encoder produces a multi-resolution representation for each molecule in a fine-to-coarse fashion, from atoms to connected motifs. Each level integrates the encoding of constituents below with the graph at that level. Our autoregressive coarse-to-fine decoder adds one motif at a time, interleaving the decision of selecting a new motif with the process of resolving its attachments to the emerging molecule. We evaluate our model on multiple molecule generation tasks, including polymers, and show that our model significantly outperforms previous state-of-the-art baselines.

Key facts. Cited by 204 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 26; 2022: 33; 2023: 47; 2024: 41; 2025: 33; 2026: 24.

204 papers.

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2022DiGress: Discrete Denoising diffusion for graph generationcited by 503
2022Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equationscited by 273
2022Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editingcited by 165
2021MARS: Markov Molecular Sampling for Multi-objective Drug Discoverycited by 125
2022SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generatorscited by 96
2023MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generationcited by 96
2022Exploring Chemical Space with Score-based Out-of-distribution Generationcited by 95
2021GraphEBM: Molecular Graph Generation with Energy-Based Modelscited by 85
2024Position: Graph Foundation Models are Already Herecited by 84
2021Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-designcited by 82
2023Autoregressive Diffusion Model for Graph Generationcited by 82
2021Graph Representation Learning in Biomedicinecited by 79

38 more

2022Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Predictioncited by 78
2021Learning to Extend Molecular Scaffolds with Structural Motifscited by 70
2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learningcited by 70
2021Generalizing Graph Neural Networks on Out-Of-Distribution Graphscited by 69
2022A Survey on Deep Graph Generation: Methods and Applicationscited by 69
2021Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generationcited by 68
2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
2023DrugAssist: A Large Language Model for Molecule Optimizationcited by 57
2021Molecule Generation by Principal Subgraph Mining and Assemblingcited by 53
2022Multi-Scale Representation Learning on Proteinscited by 52
2022Graph-based Molecular Representation Learningcited by 51
2023Rethinking Tokenizer and Decoder in Masked Graph Modeling for Moleculescited by 49
2023Sample-efficient Multi-objective Molecular Optimization with GFlowNetscited by 48
2023De Novo Molecular Generation via Connection-aware Motif Miningcited by 45
2021Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphscited by 44
2021Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Designcited by 44
2022LIMO: Latent Inceptionism for Targeted Molecule Generationcited by 40
2023Efficient and Scalable Graph Generation through Iterative Local Expansioncited by 35
2021Rapid Neural Architecture Search by Learning to Generate Graphs from Datasetscited by 33
20223DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Designcited by 33
2024Diffusion Models as Constrained Samplers for Optimization with Unknown Constraintscited by 33
2023A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretrainingcited by 32
2023ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedbackcited by 30
2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
2023Sparse Training of Discrete Diffusion Models for Graph Generationcited by 26
2022Debiased Graph Neural Networks with Agnostic Label Selection Biascited by 25
2023Learning Subpocket Prototypes for Generalizable Structure-based Drug Designcited by 25
2021Directional Message Passing on Molecular Graphs via Synthetic Coordinatescited by 24
2023Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3Dcited by 24
2022Molecular Representation Learning via Heterogeneous Motif Graph Neural Networkscited by 23
2021MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generationcited by 22
2024Molecule Generation with Fragment Retrieval Augmentationcited by 22
2022Graph Neural Networks for Moleculescited by 21
2023Domain-Agnostic Molecular Generation with Chemical Feedbackcited by 21
2024Discrete Diffusion Schr"odinger Bridge Matching for Graph Transformationcited by 21
2022Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Designcited by 19

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